HEADY: News headline abstraction through event pattern clustering

Enrique Alfonseca, Daniele Pighin and Guillermo Garrido

Abstract

This paper presents HEADY: a novel, ab- stractive approach for headline generation from news collections. From a web-scale corpus of English news, we mine syntactic patterns that a Noisy-OR model generalizes into event descriptions. At inference time, we query the model with the patterns observed in an unseen news collection, identify the event that better captures the gist of the collection and retrieve the most appropriate pattern to generate a headline. HEADY improves over a state-of-the- art open-domain title abstraction method, bridging half of the gap that separates it from extractive methods using human-generated titles in manual evaluations, and performs comparably to human-generated headlines as evaluated with ROUGE.